Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

ABSTRACT: Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

PROVIDER: PRJNA1113996 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR29099500_1.fastq.gz	Fastqsanger.gz
	SRR29099500_2.fastq.gz	Fastqsanger.gz
	SRR29099600_1.fastq.gz	Fastqsanger.gz
	SRR29099600_2.fastq.gz	Fastqsanger.gz
	SRR29099700_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 780

Similar Datasets

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age

Project description:Estimation of chronological age is particularly informative in forensic contexts. Assessment of DNA methylation status allows for the prediction of age, though the accuracy may vary across models. In this study, we started with a carefully designed discovery cohort with more elderly subjects than other age categories, to diminish the effect of epigenetic drifting. We applied multiplexing and massive parallel sequencing of targeted DNA methylation, which let us to construct a model comprising 25 CpG sites with substantially improved accuracy (MAE=2.279, R=0.92). This model is further validated by an independent cohort (MAE=2.204, 82.7% success (±5 years)). Remarkably, in a multi-center test using trace blood samples from forensic caseworks, the correct predictions (±5 years) are 91.7%. The nature of our analytical pipeline can easily be scaled up with low cost. Taken together, we propose a new age-prediction model featuring accuracy, sensitivity, high-throughput, and low cost. This model can be readily applied in both classic and newly emergent forensic contexts that require age estimation.

2025-03-19 | GSE267985 | GEO

Multiplexing and massive parallel sequencing of targeted DNA methylation to predict chronological age.

Project description: Not available

| S-EPMC11906720 | biostudies-literature

Massive Parallel Sequencing (MPS) of miRNAs in PBMCs

Project description:Massive Parallel Sequencing (MPS) of miRNAs in PBMCs

| PRJNA1062301 | ENA

Massive Parallel Sequencing (MPS) of miRNAs in PBMCs

Project description:We carried out Massive Parallel Sequencing (MPS) to reveal circulating miRNA profiles of 96 patients to distinguish the radiographic and non-radiographic axial spondyloarthritis. The sequencing platform identified 1900 miRNA species, out of which 432 miRNAs were present with the base mean, computed by DESeq2, more than 10. We identified 48 miRNAs, which were different in PBMCs in patients with axial spondyloarthritis compared to healthy controls.

2024-01-09 | GSE252708 | GEO

Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants

Project description:Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants

| PRJNA1371880 | ENA

Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [scRNA_MYOD1]

Project description:Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [scRNA_MYOD1]

| PRJNA1371884 | ENA

Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [MITE_SAM]

Project description:Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [MITE_SAM]

| PRJNA1371875 | ENA

Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_MYOD]

Project description:Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_MYOD]

| PRJNA1371876 | ENA

Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_P63noconv]

Project description:Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_P63noconv]

| PRJNA1371877 | ENA

Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants]

Project description:Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants]

| PRJNA1371878 | ENA

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data